"Using MySQL to Search a Database" matching MCP servers:

Indexes codebases using semantic embeddings for natural language search, enabling developers to find code with queries like 'how does authentication work'.

Enables AI agents to search local Markdown documents using natural language, with automatic indexing and section-level retrieval.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

Connects AI clients to MindsDB via the MySQL protocol to execute SQL queries, manage databases, and perform semantic searches within knowledge bases. It enables automated workflows through job scheduling and provides seamless integration with external data sources.

Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

A tiny RAG-lite retrieval engine that indexes files on disk and provides semantic search via MCP, returning relevant text chunks (file, line, score) without generating answers.

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

Enables Claude to search and retrieve documents from Azure AI Search indexes with intelligent summarization and analysis using LangGraph workflows and optional Google Gemini integration.

A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.

Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities.

An MCP server that enables hybrid semantic and keyword retrieval over your documents, using PostgreSQL and pgvector as the backend. It fuses rankings from both methods to provide high-quality search results to the language model.

This MCP server provides semantic document search and retrieval, enabling AI assistants to search documents, search categories, and retrieve category hierarchies using the Model Context Protocol.

MCP server that searches documents in Qdrant using embeddings from LMStudio. Takes a text query, converts it to a vector via LMStudio's OpenAI-compatible API, and performs semantic search in Qdrant.

Enables AI assistants to search, query, and manage Azure AI Search indexes using full-text, semantic, and vector search, alongside index and document operations.

An unofficial MCP server that provides semantic search capabilities for Hugging Face models and datasets, enabling Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.

Enables Claude to store and retrieve information with semantic search using Qdrant vector database, providing persistent memory for conversations, code, and documentation.

Enables semantic search across indexed documents using vector embeddings. Index GitHub repositories and URLs to perform natural language queries with AI-enhanced contextual results.

A project search MCP server based on Milvus vector database, supporting semantic search, hybrid search, and exact scalar queries.

An MCP server for semantic code search using Qdrant vector database, enabling natural language queries to find relevant code snippets across indexed codebases.